Design and development of a python-based interface for parallel calibration of SWAT+ model at large scale using dynamically dimensioned search algorithm

Authors: GAO, JUNGANG - Texas Agrilife Research; White, Michael; CERKASOVA, NATALJA - Texas Agrilife Research; Arnold, Jeffrey; ALLEN, PETER - Baylor University; Thorp, Kelly; Lee, Joon Hee; RATH, SAGARIKA - Texas Agrilife Research; CHAWANDA, CELRAY - Texas Agrilife Research

Submitted to: Journal of Environmental Modeling and Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/24/2025
Publication Date: 10/1/2025
Citation: Gao, J., White, M.J., Cerkasova, N., Arnold, J.G., Allen, P., Thorp, K.R., Lee, J., Rath, S., Chawanda, C.J. 2025. Design and development of a python-based interface for parallel calibration of SWAT+ model at large scale using dynamically dimensioned search algorithm. Journal of Environmental Modeling and Software. https://doi.org/10.1016/j.envsoft.2025.106708.
DOI: https://doi.org/10.1016/j.envsoft.2025.106708

Interpretive Summary: Efficient calibration of hydrological models is required for accurate watershed simulations and informed water resource management. This research introduces a novel approach to calibrate the SWAT+ model using the Dynamically Dimensioned Search (DDS) algorithm enhanced by parallel computing. By distributing computational tasks across multiple processors, this method dramatically reduces calibration time while maintaining high precision. A case study in the Upper Mississippi River Basin demonstrated the effectiveness of this approach and validation against observed data confirms the reliability of the calibrated model. This innovative method significantly accelerates the calibration of complex hydrologic models, enabling researchers and water managers to conduct scenario analyses and make timely decisions for sustainable water resource management.

Technical Abstract: Efficient calibration of hydrological models such as the Soil and Water Assessment Tool Plus (SWAT+) is crucial for accurate watershed simulations and informed water resource management. Traditional calibration strategies often struggle to handle the computational complexities inherent in exploring high-dimensional parameter spaces. This research introduces an approach that harnesses the power of parallel computing for SWAT+ model calibration, employing the Dynamically Dimensioned Search (DDS) algorithm. In this research, parallel computing is utilized to distribute computational tasks across multiple processors or computing nodes, significantly reducing calibration time while maintaining precision. By leveraging parallel computing resources, the DDS algorithm can explore more extensive parameter spaces in SWAT+ calibration in a reasonable timeframe. In a case study conducted in the Upper Mississippi River Basin (UMRB), we demonstrate the effectiveness of parallel computing-enabled DDS in calibrating the SWAT+ model. The algorithm efficiently navigates complex parameter spaces, leading to enhanced model performance in simulating critical hydrological processes such as streamflow dynamics, sediment yield, and nutrient transport. Validation against observed data demonstrates the performance of the calibrated model. This method offers significant benefits for large-scale hydrologic modeling, enabling researchers and water resource managers to calibrate more complex or fine-grained watershed models much more quickly. The integration of parallel computing with DDS in SWAT+ calibration facilitates accelerated model calibration and scenario analysis capabilities, enabling more timely decision-making for sustainable water management projects.